X-Git-Url: https://git.sesse.net/?p=stockfish;a=blobdiff_plain;f=README.md;h=330d19edd1f666ca90296fe0cdabe83b27e2c230;hp=0bbe4abbefbbdf1ef57dd4b1a69de2891b760abd;hb=c357c4ad6f7318234c4d745eaa6b0c4774e28741;hpb=07e6ceacd623402a8b83c834e95db77efaad3782

diff --git a/README.md b/README.md
index 0bbe4abb..330d19ed 100644
--- a/README.md
+++ b/README.md
@@ -120,14 +120,6 @@ change them via a chess GUI. This is a list of available UCI options in Stockfis
     Limit Syzygy tablebase probing to positions with at most this many pieces left
     (including kings and pawns).
 
-  * #### Contempt
-    A positive value for contempt favors middle game positions and avoids draws,
-    effective for the classical evaluation only.
-
-  * #### Analysis Contempt
-    By default, contempt is set to prefer the side to move. Set this option to "White"
-    or "Black" to analyse with contempt for that side, or "Off" to disable contempt.
-
   * #### Move Overhead
     Assume a time delay of x ms due to network and GUI overheads. This is useful to
     avoid losses on time in those cases.
@@ -183,8 +175,12 @@ on the evaluations of millions of positions at moderate search depth.
 The NNUE evaluation was first introduced in shogi, and ported to Stockfish afterward.
 It can be evaluated efficiently on CPUs, and exploits the fact that only parts
 of the neural network need to be updated after a typical chess move.
-[The nodchip repository](https://github.com/nodchip/Stockfish) provides additional
-tools to train and develop the NNUE networks. On CPUs supporting modern vector instructions
+[The nodchip repository](https://github.com/nodchip/Stockfish) provided the first version of
+the needed tools to train and develop the NNUE networks. Today, more advanced training tools are available
+in [the nnue-pytorch repository](https://github.com/glinscott/nnue-pytorch/), while data generation tools
+are available in [a dedicated branch](https://github.com/official-stockfish/Stockfish/tree/tools).
+
+On CPUs supporting modern vector instructions
 (avx2 and similar), the NNUE evaluation results in much stronger playing strength, even
 if the nodes per second computed by the engine is somewhat lower (roughly 80% of nps
 is typical).